Last but not the least, rate control in learning-based methods is different from the codec schemes too. Early learning-based methods always need to train different models for different reconstruction quality. Toderci et al. [1]} use recurrent neural networks to recursively compress residual information similar to the recursive search block in video codecs, encoding the latents of each iteration by a binary representation. Choi et al. [2]} propose a conditional convolution to make rate control easier in auto-encoder style networks without incremental inferences.
